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3D-Based Reasoning with Blocks, Support, and Stability

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4 Author(s)
Zhaoyin Jia ; Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA ; Gallagher, A. ; Saxena, A. ; Tsuhan Chen

3D volumetric reasoning is important for truly understanding a scene. Humans are able to both segment each object in an image, and perceive a rich 3D interpretation of the scene, e.g., the space an object occupies, which objects support other objects, and which objects would, if moved, cause other objects to fall. We propose a new approach for parsing RGB-D images using 3D block units for volumetric reasoning. The algorithm fits image segments with 3D blocks, and iteratively evaluates the scene based on block interaction properties. We produce a 3D representation of the scene based on jointly optimizing over segmentations, block fitting, supporting relations, and object stability. Our algorithm incorporates the intuition that a good 3D representation of the scene is the one that fits the data well, and is a stable, self-supporting (i.e., one that does not topple) arrangement of objects. We experiment on several datasets including controlled and real indoor scenarios. Results show that our stability-reasoning framework improves RGB-D segmentation and scene volumetric representation.

Published in:

Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on

Date of Conference:

23-28 June 2013

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